Assessment approach for calculating transfer alignment accuracy of SINS on moving base

Yang, Ge; Wang, Yanyong; Yang, Shujie

doi:10.1016/j.measurement.2014.03.014

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…Furthermore, the slave and master SINS are always positioned at distance from each other, and there will be relative motion between them while the vehicle turns or manoeuvres, this will introduce additional errors into the TA. The TA method based on inertial measurement matching have received extensive attention in recent years 18,[27][28][29][30][31][32][33][34][35][36][37][38][39][40] , which can be depicted in Fig. 1.…”

Section: Transfer Alignment Of Sinsmentioning

confidence: 99%

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Dai

Tang³

et al. 2019

Def. Sc. Jl.

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Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy. 7. dai, h.; dai, S.; Cong, y.; Zhao, g. & wu, g. Rapid transfer alignment of laser sins using quaternion based angular measurement.

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Section: Transfer Alignment Of Sinsmentioning

confidence: 99%

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Dai

Tang³

et al. 2019

Def. Sc. Jl.

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“…In this test, the relative attitude between MINS and SINS is invariable since they were rigidly bolted on the steel plate, i.e.,

can be considered as a constant vector. Therefore, the CKF-based fixed-point smoother was employed in the accuracy assessment procedure, on account of the nonlinear characteristic of the model description [ 27 , 28 ]. As is shown in Figure 13 , the accuracy assessment procedure was initiated at Point B and ended at Point C (700 s), where the GPS functioned well and the Gaussian assumption for the observation noise is accredited.…”

Section: Van Testmentioning

confidence: 99%

Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INS

Zhou

Guo

2017

Sensors

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The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estimation accuracy of misalignment angles. This study aims to develop a novel robust nonlinear filter, namely the stochastic integration H∞ filter (SIH∞F) for improving both the accuracy and robustness of RTA. In this new nonlinear H∞ filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H∞ filter for the first time, and the resulting SIH∞F simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIH∞F and previously well-known methodologies are carried out by means of numerical simulation and a van test. The results demonstrate that the newly-proposed method outperforms the cubature H∞ filter. Moreover, the SIH∞F inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty.

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“…MAE can reflect the possible estimation error range of estimation methods; MRE stands for the ensemble estimation error; and RMSE reflects the sensitivity and extreme value effect of the estimation results. The calculation formulae of these three evaluation criteria are described as follows [25,26]:

M A E = \frac{1}{n} true \sum_{i = 1}^{n} A B S false(X_{m, i} - X_{e, i} false)

M R E = \frac{1}{n} true \sum_{i = 1}^{n} false[A B S false(X_{m, i} - X_{e, i} false) / X_{m, i} false]

R M S E = \sqrt{\frac{1}{n} true \sum_{i = 1}^{n} {false(X_{m, i} - X_{e, i} false)}^{2}}

where n is the number of the testing points and

X_{m, i}

X_{e, i}

are the measured and estimated gravity disturbance values, respectively.

A B S

is the symbol of the absolute value function.…”

Section: Numerical Testmentioning

confidence: 99%

A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”

Zhou

Yang

Cai

et al. 2016

Sensors

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In recent years, with the emergency of high precision inertial sensors (accelerometers and gyros), gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper presents preliminary results concerning the effect of gravity disturbance on INS. Meanwhile, this paper proposes a novel gravity compensation method for high-precision INS, which estimates the gravity disturbance on the track using the extreme learning machine (ELM) method based on measured gravity data on the geoid and processes the gravity disturbance to the height where INS has an upward continuation, then compensates the obtained gravity disturbance into the error equations of INS to restrain the INS error propagation. The estimation accuracy of the gravity disturbance data is verified by numerical tests. The root mean square error (RMSE) of the ELM estimation method can be improved by 23% and 44% compared with the bilinear interpolation method in plain and mountain areas, respectively. To further validate the proposed gravity compensation method, field experiments with an experimental vehicle were carried out in two regions. Test 1 was carried out in a plain area and Test 2 in a mountain area. The field experiment results also prove that the proposed gravity compensation method can significantly improve the positioning accuracy. During the 2-h field experiments, the positioning accuracy can be improved by 13% and 29% respectively, in Tests 1 and 2, when the navigation scheme is compensated by the proposed gravity compensation method.

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Assessment approach for calculating transfer alignment accuracy of SINS on moving base

Cited by 13 publications

References 12 publications

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INS

A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”

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